Dendrite classification in rat hippocampal neurons according to signal propagation properties. Observation by multichannel optical recording in cultured neuronal networks

Influence of a nanorod molecular layer on the biological activity of neuronal cells. A semiclassical model for complex solid/liquid interfaces with carbon nanotubes

A general account of electric effects is given for a biological phase interacting with a nanorod molecular layer by means of the formed hard-soft and solid-liquid interfaces. In particular, the frequency enhancement previously detected for the spontaneous activity of neuronal cultures interfaced with carbon nanotubes is quantitatively explained upon a quantum/semiclassical description, where the duration of a biological signal is viewed as the (average) lifetime of a decaying state (or population of states), and the effect of the carbon phase as a linewidth broadening. Four contributions were principally accounted for, one biological, for the synaptic strength, one electrochemical, for the overall capacitance increase implied by the nanotube double layers, one geometric, for the typical scales ruling the electron and ion conduction mechanisms, and one electromagnetic-like, translating the membrane polarization changes. These calculations predict an enhancement factor equal on average to ≃6.39, against a former experimental value ≃6.08.

Long-term activity-dependent plasticity of action potential propagation delay and amplitude in cortical networks

BACKGROUND

The precise temporal control of neuronal action potentials is essential for regulating many brain functions. From the viewpoint of a neuron, the specific timings of afferent input from the action potentials of its synaptic partners determines whether or not and when that neuron will fire its own action potential. Tuning such input would provide a powerful mechanism to adjust neuron function and in turn, that of the brain. However, axonal plasticity of action potential timing is counter to conventional notions of stable propagation and to the dominant theories of activity-dependent plasticity focusing on synaptic efficacies.

METHODOLOGY/PRINCIPAL FINDINGS

Here we show the occurrence of activity-dependent plasticity of action potential propagation delays (up to 4 ms or 40% after minutes and 13 ms or 74% after hours) and amplitudes (up to 87%). We used a multi-electrode array to induce, detect, and track changes in propagation in multiple neurons while they adapted to different patterned stimuli in controlled neocortical networks in vitro. The changes did not occur when the same stimulation was repeated while blocking ionotropic gabaergic and glutamatergic receptors. Even though induction of changes in action potential timing and amplitude depended on synaptic transmission, the expression of these changes persisted in the presence of the synaptic receptor blockers.

CONCLUSIONS/SIGNIFICANCE

We conclude that, along with changes in synaptic efficacy, propagation plasticity provides a cellular mechanism to tune neuronal network function in vitro and potentially learning and memory in the brain.

Hypercapnia modulates synaptic interaction of cultured brainstem neurons

CO(2) is an important metabolic product whose concentrations are constantly monitored by CO(2) chemoreceptors. However, the high systemic CO(2) sensitivity may not be achieved by the CO(2) chemoreceptors without neuronal network processes. To show modulation of network properties during hypercapnia, we studied brainstem neurons dissociated from embryonic rats (P17-19) in multielectrode arrays (MEA) after initial period (3 weeks) of culture. Spike trains of 33,622 pairs of units were analyzed using peri-event histograms (PEH). The amplitude of peri-central peaks between two CO(2)-stimulated units increased and the peak latency decreased during hypercapnia. Similar enhancement of synaptic strength was observed in those sharing a common input. These phenomena were not seen in CO(2)-unresponsive neurons. The amplitude of peri-central peaks between two CO(2) inhibited units also increased without changing latency. Over 60% CO(2)-stimulated neurons studied received mono-/oligosynaptic inputs from other CO(2)-stimulated cells, whereas only approximately 10% CO(2)-unresponsive neurons had such synaptic inputs. A small number of brainstem neurons showed electrical couplings. The coupling efficiency of CO(2)-stimulated but not CO(2)-unresponsive units was suppressed by approximately 50% with high PCO(2). Inhibitory synaptic projections were also found, which was barely affected by hypercapnia. Consistent with the strengthening of excitatory synaptic connections, CO(2) sensitivity of post-synaptic neurons was significantly higher than presynaptic neurons. The difference was eliminated with blockade of presynaptic input. Based on these indirect assessments of synaptic interaction, our PEH analysis suggests that hypercapnia appears to modulate excitatory synaptic transmissions, especially those between CO(2)-stimulated neurons.

Detection, Stimulation, and Inhibition of Neuronal Signals with High-Density Nanowire Transistor Arrays

We report electrical properties of hybrid structures consisting of arrays of nanowire field-effect transistors integrated with the individual axons and dendrites of live mammalian neurons, where each nanoscale junction can be used for spatially resolved, highly sensitive detection, stimulation, and/or inhibition of neuronal signal propagation. Arrays of nanowire-neuron junctions enable simultaneous measurement of the rate, amplitude, and shape of signals propagating along individual axons and dendrites. The configuration of nanowire-axon junctions in arrays, as both inputs and outputs, makes possible controlled studies of partial to complete inhibition of signal propagation by both local electrical and chemical stimuli. In addition, nanowire-axon junction arrays were integrated and tested at a level of at least 50 "artificial synapses" per neuron.

Effects of random external background stimulation on network synaptic stability after tetanization: a modeling study

We constructed a simulated spiking neural network model to investigate the effects of random background stimulation on the dynamics of network activity patterns and tetanus induced network plasticity. The simulated model was a "leaky integrate-and-fire" (LIF) neural model with spike-timing-dependent plasticity (STDP) and frequency-dependent synaptic depression. Spontaneous and evoked activity patterns were compared with those of living neuronal networks cultured on multi-electrode arrays. To help visualize activity patterns and plasticity in our simulated model, we introduced new population measures called Center of Activity (CA) and Center of Weights (CW) to describe the spatio-temporal dynamics of network-wide firing activity and network-wide synaptic strength, respectively. Without random background stimulation, the network synaptic weights were unstable and often drifted after tetanization. In contrast, with random background stimulation, the network synaptic weights remained close to their values immediately after tetanization. The simulation suggests that the effects of tetanization on network synaptic weights were difficult to control because of ongoing synchronized spontaneous bursts of action potentials, or "barrages." Random background stimulation helped maintain network synaptic stability after tetanization by reducing the number and thus the influence of spontaneous barrages. We used our simulated network to model the interaction between ongoing neural activity, external stimulation and plasticity, and to guide our choice of sensory-motor mappings for adaptive behavior in hybrid neural-robotic systems or "hybrots."

Dendritic integration in a recurrent network

Classical nonlinear cable theory is appropriate for the unmyelinated axonal membrane because voltage-dependent ion channels are densly distributed, but dendrites with a sparse density distribution of voltage-dependent ion channels show "weakly" excitable membrane properties. Therefore, a model for "weakly" active dendrites is presented by introducing voltage-dependent ion channels at discrete locations along the dendritic cable. This provides an alternative representation for the investigation of regenerative potentials in dendrites in order to explore how active dendrites influence synaptic integration. As an example, we consider a two-neuron recurrent network of biophysically distinct conductance-based model neurons with discrete clusters of persistent sodium channels. Analytical solutions, expressed in terms of a Volterra series expansion for the voltage in response to a suprathreshold input current at the soma of one neuron, are obtained to investigate dendritic spikes, and the effect of backpropagation on distal dendritic spike-like potentials.

Population coding in spike trains of simultaneously recorded retinal ganglion cells

To achieve a better understanding of the parallel information processing that takes place in the nervous system, many researchers have recently begun to use multielectrode techniques to obtain high spatial- and temporal-resolution recordings of the firing patterns of neural ensembles. Apart from the complexities of acquiring and storing single unit responses from large numbers of neurons, the multielectrode technique has provided new challenges in the analysis of the responses from many simultaneously recorded neurons. This paper provides insights into the problem of coding/decoding of retinal images by ensembles of retinal ganglion cells. We have simultaneously recorded the responses of 15 ganglion cells to visual stimuli of various intensities and wavelengths and analyzed the data using discriminant analysis. Models of stimulus encoding were generated and discriminant analysis used to estimate the wavelength and intensity of the stimuli. We find that the ganglion cells we have recorded from are non-redundant encoders of these stimulus features. While single ganglion cells are poor classifiers of the stimulus parameters, examination of the responses of only a few ganglion cells greatly enhances our ability to specify the stimulus wavelength and intensity. Of the parameters studied, we find that the rate of firing of the ganglion cells provides the most information about these stimulus parameters, while the timing of the first action potential provides almost as much information. While we are not suggesting that the brain is using these variables, our results show how a population of sensory neurons can encode stimulus features and suggest that the brain could potentially deduce reliable information about stimulus features from response patterns of retinal ganglion cell populations.

Theoretical analysis of the amplification of synaptic potentials by small clusters of persistent sodium channels in dendrites

We extend on the work developed by R.R. Poznanski and J. Bell from a linearized somatic persistent sodium current source to a non-linear representation of the dendritic Na(+)P current source associated with a small number of persistent sodium channels. The main objective is to investigate the modulation in the amplification of excitatory postsynaptic potentials (EPSPs) in dendrites studded with persistent sodium channels. The relation between membrane potential (V) and persistent sodium current density (I(NaP)) is approximated heuristically with a sigmoidal function and the resultant cable equation is solved analytically using a regular perturbation expansion and Green's function techniques. The transient simulated (non-evoked) response is found as a result of current injection in the form of synaptically induced voltage change located at a distance from the recording site in a cable with a uniform distribution of ion channel densities per unit length of cable (the so-called 'hot-spots') and with the conductance of each hot-spot (i.e., number of channels per hot-spot) assumed to be a constant. The results show an amplification in the observed EPSPs to be compatible with the experimentally derived estimates, and in addition a saturation in the amplification is observed indicating an optimum number of ionic channels.